In this section, we compare the performance of ASkewSGD [W1/A32] on a large dataset and compare it to BinaryConnect [1]}, [2]}, Mirror Descent [3]}, AdaSTE [4]}, a standard full-precision NN, and a hypersphere-projected full-precision NN. To ensure a fair comparison, we compare the different methods using the same NN architecture. Moreover, we do not add bias in any layer, but introduce batch normalisation (without learning parameters) after each layer. The last connected layer is kept in full precision - a standard practice in BNN -. Contrary to [5]}, [6]}, we have kept the first convolutional layer binary. We do not use layerwise scalar contrary to [7]}.
